user
Task: Explain the difference between gradient descent and a genetic algorithms.


assistant
Gradient descent and genetic algorithms are both optimization techniques used to arrive at an optimal solution to a problem. Gradient descent is a iterative optimization technique which moves in the direction of the greatest slope of a given function. It is a single step optimization technique and is used for supervised learning problems. 

Genetic algorithms are a type of search algorithm that use the principles of evolution, including selection, mutation and recombination, to evolve a solution to a problem. This is a multi-step optimization technique and is used for both supervised and unsupervised learning problems. It uses a population of solutions based on randomly generated rules to search for an optimal solution. The genetic algorithm evaluates each solution in the population and then selects which ones should proceed to the next generation.